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4 commits

Author SHA1 Message Date
a89cc53fed transformers+vllm: 5-MOAD rescan; transformers-0004 CWE-407 all_special_ids per-loop rebuild in wav2vec2/esm
Rescan both targets against all 5 MOADs (2026-04-03).

New defect:
- transformers-0004: wav2vec2, wav2vec2_phoneme, esm tokenizers call
  self.all_special_ids/@property inside per-token decode loops, rebuilding
  list every iteration. O(T) -> O(1) fix: cache set before loop.
  wav2vec2_phoneme also has type mismatch (str vs list[int]), making
  the check always False, leaking special tokens.
  9/9 unit tests PASS.

Existing defects confirmed still present (not re-filed):
- transformers-0001/0002/0003: unchanged from 2026-03-31 scan.
- vllm-0001/0002: unchanged from 2026-03-31 scan.

MOAD-0002/0003/0004/0005: CLEAN on both targets (see SCAN-2026-04-03.md).

SCAN-TODO.md: marked transformers and vllm as complete with full summary.

Also includes UNDF stamps on jicofo-0001, jicofo-0002, langchain-0002 patches
from prior generate_undf.py run.
2026-04-03 15:32:37 -04:00
81bef63b2e transformers+vllm: 3 new defects, all 5 MOADs scanned
transformers-0002: MOAD-0004 (CWE-312) regnet convert script logs HF_TOKEN verbatim
transformers-0003: MOAD-0001 (CWE-407) convert_tokens_to_string O(T×S) list scan
  - marian, m2m_100, speech_to_text, siglip, gpt_sw3 all affected
  - all_special_tokens is list[str]; fix: cache set() before loop; 5x speedup

vllm-0002: MOAD-0001 (CWE-407) Grok2Tokenizer O(N×V) dict.values() scan
  - decode() and convert_ids_to_tokens() use .values() view per token
  - sibling Mistral tokenizer already uses frozenset correctly
  - fix: add _special_token_ids frozenset at __init__; 10x speedup at N=2048, V=200

MOADs 0002/0003/0005 CLEAN for both repos
2026-03-31 20:17:09 -04:00
7e717432dd undf: assign UNDF-2026-000000874; stamp vllm-0001 patch 2026-03-30 16:42:12 -04:00
80b26e82b6 ml-inference scan: vllm-0001 LoRA convert_mapping list.index O(B*L); ollama+langchain CLEAN
vllm-0001: punica_wrapper/utils.py convert_mapping() calls
lora_index_to_id.index(x) per token in batch — O(B*L) where
B=batch_size, L=loaded_loras. Code has "TODO index can be slow"
comment. Fix: pre-build dict for O(1) lookup. 7x at B=2000/L=64.

Ollama: all slices.Contains on bounded slices (1-8 items).
LangChain: orchestration code, all membership bounded by k param.
2026-03-30 16:41:47 -04:00